The present invention relates generally to detecting turn faults in the stator of an alternating current (AC) machine.
Stator turn-to-turn shorts are one of the more prevalent and potentially destructive electrical faults in inverter-driven AC machines such as induction motors, for example. Arising primarily from insulation degradation (through contamination or abnormal thermal, mechanical, electrical or other environmental stresses), these localized faults produce thermal hot spots that foster progressive degradation that can mature into turn-to-ground faults. On-line detection of early stages of electrical failure modes is crucial to promoting safe and economical use of induction motors in industrial applications. Economic efficiency of turn fault detection is of prime concern, particularly on small machines where little resources can be allocated for motor monitoring and where a monitoring device or system has to operate in a stand-alone mode. To best preserve the stator, a marketable system should be able to detect a motor fault and subsequently interrupt the power within a minimum number of cycles of the electrical excitation. For larger machines where the potential damage is greater, fractions of a cycle under fault can translate into great financial losses.
Pre-processing data in an intelligence-based scheme requires expensive and inconvenient levels of data collection and creates a susceptibility to false alarms that arise from failure to learn the entire operating regime. A candidate detection scheme should avoid extensive reliance upon real-time comparisons to learned conditions that may not incorporate all acceptable and unacceptable machine states. Furthermore, such elaborate intelligence-based schemes usually depend upon more expensive digital signal processing hardware that can limit their marketability in smaller or remote machines.
In addition to signal processing hardware, another physical consideration involves sensors. As practicality and economic constraints discourage the use of delicate machine-embedded devices, the only inherent and readily accessible alternative sources of information regarding a machine's health are the machine's terminal voltages and currents. Conventional machine monitoring techniques with machine terminal currents and voltages require significant digital signal processing power. A common example for turn fault detection is the decomposition and subsequent analysis of the three phase currents and voltages into the positive, negative, and zero sequence values. Such turn fault detection techniques include algorithms that focus on monitoring the negative sequence voltage and current which arise from unbalanced machine operation and/or unbalanced windings. Harmonics and frequency variations can interfere with accurate negative sequence value calculations.